{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:12:15Z","timestamp":1743106335295,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030736705"},{"type":"electronic","value":"9783030736712"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73671-2_27","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:12:10Z","timestamp":1625569930000},"page":"316-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Electricity Sales Forecast Model Based on Big Data"],"prefix":"10.1007","author":[{"given":"Kai","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kejiang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"27_CR1","unstructured":"Pan, Y., Wei, Y.: Strategy of electricity sales and retail based on double-layer game. In: 2020 International Symposium on Frontiers of Economics and Management Science (FEMS 2020), Dalian, Liaoning, China, pp. 172\u2013177. Institute of Management Science and Industrial Engineering (2020)"},{"issue":"06","key":"27_CR2","first-page":"166","volume":"36","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Song, K., Wang, X.: Analysis of economic prosperity index based on big data of electricity. Telecommun. Sci. 36(06), 166\u2013171 (2020)","journal-title":"Telecommun. Sci."},{"issue":"1","key":"27_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-019-0089-x","volume":"8","author":"S Siuly","year":"2020","unstructured":"Siuly, S., Zhang, X.: Guest Editorial: special issue on \u201cApplication of artificial intelligence in health research.\u201d Health Inf. Sci. Syst. 8(1), 1 (2020)","journal-title":"Health Inf. Sci. Syst."},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.energy.2018.01.169","volume":"149","author":"S Ding","year":"2018","unstructured":"Ding, S., Hipel, K., Dang, Y.: Forecasting China\u2019s electricity consumption using a new grey prediction model. Energy 149, 314\u2013328 (2018)","journal-title":"Energy"},{"issue":"10","key":"27_CR5","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1057\/s41274-016-0150-y","volume":"68","author":"Y Hu","year":"2017","unstructured":"Hu, Y.: Electricity consumption prediction using a neural-network-based grey forecasting approach. J. Oper. Res. Soc. 68(10), 1259\u20131264 (2017)","journal-title":"J. Oper. Res. Soc."},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1016\/j.egypro.2019.02.027","volume":"158","author":"Z Zheng","year":"2019","unstructured":"Zheng, Z., Chen, H., Luo, X.: Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network. Energy Proc. 158, 2713\u20132718 (2019)","journal-title":"Energy Proc."},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1016\/j.apenergy.2019.01.113","volume":"238","author":"J Bedi","year":"2019","unstructured":"Bedi, J., Toshniwal, D.: Deep learning framework to forecast electricity demand. Appl. Energy 238, 1312\u20131326 (2019)","journal-title":"Appl. Energy"},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.egypro.2011.12.895","volume":"14","author":"A Marvuglia","year":"2012","unstructured":"Marvuglia, A., Messineo, A.: Using recurrent artificial neural networks to forecast household electricity consumption. Energy Proc. 14, 45\u201355 (2012)","journal-title":"Energy Proc."},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1016\/j.energy.2018.10.073","volume":"167","author":"L Tang","year":"2019","unstructured":"Tang, L., Wang, X., Wang, X., Shao, C., Liu, S., Tian, S.: Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory. Energy 167, 1144\u20131154 (2019)","journal-title":"Energy"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Research on the method of forecasting annual electricity sales based on co-integration theory. In: IOP Conference Series Materials Science and Engineering, vol. 394, no. 4 (2018)","DOI":"10.1088\/1757-899X\/394\/4\/042113"},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"107662","DOI":"10.1016\/j.anucene.2020.107662","volume":"148","author":"Y Yu","year":"2020","unstructured":"Yu, Y., Peng, M., Wang, H., Ma, Z., Li, W.: Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant. Ann. Nucl. Energy 148, 107662 (2020)","journal-title":"Ann. Nucl. Energy"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"107528","DOI":"10.1016\/j.apacoust.2020.107528","volume":"171","author":"Z Soumaya","year":"2021","unstructured":"Soumaya, Z., Taoufiq, B., Benayad, N., Yunus, K., Abdelkrim, A.: The detection of Parkinson disease using the genetic algorithm and SVM classifier. Appl. Acoust. 171, 107528 (2021)","journal-title":"Appl. Acoust."},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.neunet.2020.05.013","volume":"128","author":"H Allen","year":"2020","unstructured":"Allen, H., James, H., Jonathan, D.: Embedding and approximation theorems for echo state networks. Neural Netw. Off. J. Int. Neural Netw. Soc. 128, 234\u2013247 (2020)","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"issue":"6","key":"27_CR14","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1002\/for.2663","volume":"39","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Xie, X., Zhang, T., Bai, J., Hou, M.: A deep residual compensation extreme learning machine and applications. J. Forecast. 39(6), 986\u2013999 (2020)","journal-title":"J. Forecast."}],"container-title":["Lecture Notes in Computer Science","Cyberspace Safety and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73671-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:28:00Z","timestamp":1625570880000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73671-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030736705","9783030736712"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73671-2_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyberspace Safety and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"css2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.hainanu.edu.cn\/scscs\/css2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"82","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}